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Noise Removal) Digital Image Processing Image Restoration and Reconstruction (Noise Removal) Christophoros Nikou [email protected] University of Ioannina - Department of Computer Science and Engineering Image Restoration and 2 Reconstruction Things which we see are not by themselves what we see… It remains completely unknown to us what the objects may be by themselves and apart from the receptivity of our senses. We know nothing but our manner of perceiving them. Immanuel Kant C. Nikou – Digital Image Processing (E12) 3 Contents In this lecture we will look at image restoration techniques used for noise removal – What is image restoration? – Noise and images – Noise models – Noise removal using spatial domain filtering – Noise removal using frequency domain filtering C. Nikou – Digital Image Processing (E12) 4 What is Image Restoration? Image restoration attempts to restore images that have been degraded – Identify the degradation process and attempt to reverse it – Similar to image enhancement, but more objective C. Nikou – Digital Image Processing (E12) 5 Noise and Images The sources of noise in digital images arise during image acquisition (digitization) and transmission – Imaging sensors can be affected by ambient conditions – Interference can be added to an image during transmission C. Nikou – Digital Image Processing (E12) 6 Noise Model We can consider a noisy image to be modelled as follows: g(x, y) f (x, y) (x, y) where f (x, y) is the original image pixel, η(x, y) is the noise term and g(x, y) is the resulting noisy pixel If we can estimate the noise model we can figure out how to restore the image C. Nikou – Digital Image Processing (E12) 7 Noise Models (cont...) There are many different Gaussian Rayleigh models for the image noise term η(x, y): – Gaussian • Most common model Erlang Exponential – Rayleigh – Erlang (Gamma) – Exponential Uniform – Uniform Impulse – Impulse • Salt and pepper noise Images taken from Gonzalez & Woods, Digital Image Processing (2002) Processing Image Digital & Woods, fromGonzalez taken Images C. Nikou – Digital Image Processing (E12) 8 Noise Example • The test pattern to the right is ideal for demonstrating the addition of noise • The following slides will show the result of adding noise Image based on various models to this image Histogram to go here Images taken from Gonzalez & Woods, Digital Image Processing (2002) Processing Image Digital & Woods, fromGonzalez taken Images C. Nikou – Digital Image Processing (E12) Histogram 9 Noise Example (cont…) Images taken from Gonzalez & Woods, Digital Image Processing (2002) Processing Image Digital & Woods, fromGonzalez taken Images C. Nikou – Digital Image Processing (E12) 10 Noise Example (cont…) Images taken from Gonzalez & Woods, Digital Image Processing (2002) Processing Image Digital & Woods, fromGonzalez taken Images C. Nikou – Digital Image Processing (E12) Restoration in the presence of noise 11 only • We can use spatial filters of different kinds to remove different kinds of noise • The arithmetic mean filter is a very simple one and is calculated as follows: 1 fˆ(x, y) g(s,t) mn (s,t)Sxy 1 1 1 This is implemented as the /9 /9 /9 simple smoothing filter 1/ 1/ 1/ 9 9 9 It blurs the image. 1 1 1 /9 /9 /9 C. Nikou – Digital Image Processing (E12) Restoration in the presence of noise 12 only (cont.) • There are different kinds of mean filters all of which exhibit slightly different behaviour: – Geometric Mean – Harmonic Mean – Contraharmonic Mean C. Nikou – Digital Image Processing (E12) Restoration in the presence of noise 13 only (cont.) Geometric Mean: 1 mn fˆ(x, y) g(s,t) (s,t)Sxy • Achieves similar smoothing to the arithmetic mean, but tends to lose less image detail. C. Nikou – Digital Image Processing (E12) Restoration in the presence of noise 14 only (cont.) Harmonic Mean: mn fˆ(x, y) 1 g(s,t) (s,t)Sxy • Works well for salt noise, but fails for pepper noise. • Also does well for other kinds of noise such as Gaussian noise. C. Nikou – Digital Image Processing (E12) Restoration in the presence of noise 15 only (cont.) Contraharmonic Mean: g(s,t)Q1 fˆ(x, y) (s,t)Sxy g(s,t)Q (s,t)Sxy • Q is the order of the filter. • Positive values of Q eliminate pepper noise. • Negative values of Q eliminate salt noise. • It cannot eliminate both simultaneously. C. Nikou – Digital Image Processing (E12) 16 Noise Removal Examples Image Original image corrupted by Gaussian noise 3x3 Geometric 3x3 Mean Filter Arithmetic (less blurring Mean Filter than AMF, the image is sharper) Images taken from Gonzalez & Woods, Digital Image Processing (2002) Processing Image Digital & Woods, fromGonzalez taken Images C. Nikou – Digital Image Processing (E12) 17 Noise Removal Examples (cont…) Image corrupted by pepper noise at 0.1 Filtering with a 3x3 Contraharmonic Filter with Q=1.5 Images taken from Gonzalez & Woods, Digital Image Processing (2002) Processing Image Digital & Woods, fromGonzalez taken Images C. Nikou – Digital Image Processing (E12) 18 Noise Removal Examples (cont…) Image corrupted by salt noise at 0.1 Filtering with a 3x3 Contraharmonic Filter with Q=-1.5 Images taken from Gonzalez & Woods, Digital Image Processing (2002) Processing Image Digital & Woods, fromGonzalez taken Images C. Nikou – Digital Image Processing (E12) 19 Contraharmonic Filter: Here Be Dragons • Choosing the wrong value for Q when using the contraharmonic filter can have drastic results Pepper noise filtered by Salt noise filtered by a a 3x3 CF with Q=-1.5 3x3 CF with Q=1.5 Images taken from Gonzalez & Woods, Digital Image Processing (2002) Processing Image Digital & Woods, fromGonzalez taken Images C. Nikou – Digital Image Processing (E12) 20 Order Statistics Filters • Spatial filters based on ordering the pixel values that make up the neighbourhood defined by the filter support. • Useful spatial filters include – Median filter – Max and min filter – Midpoint filter – Alpha trimmed mean filter Images taken from Gonzalez & Woods, Digital Image Processing (2002) Processing Image Digital & Woods, fromGonzalez taken Images C. Nikou – Digital Image Processing (E12) 21 Median Filter Median Filter: fˆ( x , y ) median{ g ( s , t )} (,)s t Sxy • Excellent at noise removal, without the smoothing effects that can occur with other smoothing filters. • Particularly good when salt and pepper noise is present. C. Nikou – Digital Image Processing (E12) 22 Max and Min Filter Max Filter: fˆ(x, y) max {g(s,t)} (s,t)Sxy Min Filter: fˆ(x, y) min {g(s,t)} (s,t)Sxy • Max filter is good for pepper noise and Min filter is good for salt noise. C. Nikou – Digital Image Processing (E12) 23 Midpoint Filter Midpoint Filter: 1 fˆ(x, y) max {g(s,t)} min {g(s,t)} (s,t)S 2 (s,t)Sxy xy • Good for random Gaussian and uniform noise. C. Nikou – Digital Image Processing (E12) 24 Alpha-Trimmed Mean Filter Alpha-Trimmed Mean Filter: ˆ 1 f (x, y) gr (s,t) mn d (s,t)Sxy • We can delete the d/2 lowest and d/2 highest grey levels. • So gr(s, t) represents the remaining mn – d pixels. C. Nikou – Digital Image Processing (E12) 25 Noise Removal Examples Salt And 1 pass with a Pepper at 0.2 3x3 median 2 passes with 3 passes with a 3x3 median a 3x3 median Repeated passes remove the noise better but also blur the image Images taken from Gonzalez & Woods, Digital Image Processing (2002) Processing Image Digital & Woods, fromGonzalez taken Images C. Nikou – Digital Image Processing (E12) 26 Noise Removal Examples (cont…) Image Image corrupted corrupted by Pepper by Salt noise noise Filtering Filtering above above with a 3x3 with a 3x3 Max Filter Min Filter Images taken from Gonzalez & Woods, Digital Image Processing (2002) Processing Image Digital & Woods, fromGonzalez taken Images C. Nikou – Digital Image Processing (E12) 27 Noise Removal Examples (cont…) Image corrupted Image further corrupted by uniform noise by Salt and Pepper noise Filtering by a 5x5 Filtering by a 5x5 Arithmetic Mean Filter Geometric Mean Filter Filtering by a 5x5 Filtering by a 5x5 Alpha- Median Filter Trimmed Mean Filter (d=5) Images taken from Gonzalez & Woods, Digital Image Processing (2002) Processing Image Digital & Woods, fromGonzalez taken Images C. Nikou – Digital Image Processing (E12) 28 Adaptive Filters • The filters discussed so far are applied to an entire image without any regard for how image characteristics vary from one point to another. • The behaviour of adaptive filters changes depending on the characteristics of the image inside the filter region. • We will take a look at the adaptive median filter. C. Nikou – Digital Image Processing (E12) 29 Adaptive Median Filtering • The median filter performs relatively well on impulse noise as long as the spatial density of the impulse noise is not large. • The adaptive median filter can handle much more spatially dense impulse noise, and also performs some smoothing for non-impulse noise. C. Nikou – Digital Image Processing (E12) 30 Adaptive Median Filtering (cont…) • The key to understanding the algorithm is to remember that the adaptive median filter has three purposes: – Remove impulse noise – Provide smoothing of other noise – Reduce distortion (excessive thinning or thickenning of object boundaries). C. Nikou – Digital Image Processing (E12) 31 Adaptive Median Filtering (cont…) • In the adaptive median filter, the filter size changes depending on the characteristics of the image. • Notation: – Sxy = the support of the filter centerd at (x, y) – zmin = minimum grey level in Sxy – zmax = maximum grey level in Sxy – zmed = median of grey levels in Sxy – zxy = grey level at coordinates (x, y) – Smax =maximum allowed size of Sxy C. Nikou – Digital Image Processing (E12) 32 Adaptive Median Filtering (cont…) Stage A: A1 = zmed – zmin A2 = zmed – zmax If A1 > 0 and A2 < 0, Go to stage B Else increase the window size If window size ≤ Smax repeat stage A Else output zmed Stage B: B1 = zxy – zmin B2 = zxy – zmax If B1 > 0 and B2 < 0, output zxy Else output zmed C.
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